import sys
sys.path.append("..")
sys.path.append("../..")
from RumdetecFramework.BaseRumorFramework import RumorDetection
from Dataloader.twitterloader import TwitterSet
from Dataloader.dataloader_utils import Lemma_Factory, Sort_data
from SentModel.Sent2Vec import *
from PropModel.SeqPropagation import *
import torch.nn as nn
from torch.utils.data import DataLoader
from sklearn.feature_extraction.text import TfidfVectorizer
import os
import pickle

if len(sys.argv) !=1:
    os.environ['CUDA_VISIBLE_DEVICES'] = sys.argv[1]

def obtain_LSTMRD(pretrained_vectorizer):
    lvec = TFIDFBasedVec(pretrained_vectorizer, 20, embedding_size=300, w2v_dir="../../saved/glove_en/")
    prop = GRUModel(sent_hidden_size=300, prop_hidden_size=100, num_layers=1, dropout_prob=0.2)
#     prop = LSTMModel(sent_hidden_size=100, prop_hidden_size=100, num_layers=1, dropout_prob=0.2)
    cls = nn.Linear(100, 2)
    LSTMRD = RumorDetection(lvec, prop, cls, batch_size=20, grad_accum_cnt=1)
    return LSTMRD

def obtain_Sort_set(tr_prefix, dev_prefix, te_prefix):
    tr_set = TwitterSet()
    tr_set.load_data_fast(data_prefix=tr_prefix, min_len=5)
    dev_set = TwitterSet()
    dev_set.load_data_fast(data_prefix=dev_prefix)
    te_set = TwitterSet()
    te_set.load_data_fast(data_prefix=te_prefix)
    return tr_set, dev_set, te_set

i = 2
tr, dev, te = obtain_Sort_set("../../data/twitter_tr%d"%i, "../../data/twitter_dev%d"%i, "../../data/twitter_te%d"%i)

Tf_Idf_twitter_file = "../../saved/TfIdf_twitter.pkl"
if os.path.exists(Tf_Idf_twitter_file):
    with open(Tf_Idf_twitter_file, "rb") as fr:
        tv = pickle.load(fr)
else:
    lemma = Lemma_Factory()
    corpus = [" ".join(lemma(txt)) for data in [tr, dev, te] for ID in data.data_ID for txt in data.data[ID]['text']]
    tv = TfidfVectorizer(use_idf=True, smooth_idf=True, norm=None)
    _ = tv.fit_transform(corpus)

tr, dev, te = Sort_data(tr, dev, te)
tr.filter_short_seq(min_len=5)
tr.trim_long_seq(10)


print("%s : (dev event)/(test event)/(train event) = %3d/%3d/%3d" % (te.data[te.data_ID[0]]['event'], len(dev), len(te), len(tr)))
print("\n\n===========Sort Train===========\n\n")
model = obtain_LSTMRD(tv)
model.train_iters(tr, dev, te, max_epochs=100,
                log_dir="../../logs/", log_suffix="_GRURD_%s"%(te.data[te.data_ID[0]]['event']),
                model_file="../../saved/TFIDF_GRURD_Sort.pkl")
te_loader = DataLoader(te,
                       batch_size=5,
                       shuffle=False,
                       collate_fn=te.collate_raw_batch)
rst = model.valid(te_loader, pretrained_file="../../saved/TFIDF_GRURD_Sort.pkl", all_metrics=True)
print("##Validation On Test Dataset####  te_acc:%3.4f, te_loss:%3.4f, te_prec:%3.4f, te_recall:%3.4f, te_f1:%3.4f"%rst)

# for i in range(5):
#     tr, dev, te = obtain_Sort_set("../../data/twitter_tr%d" % i, "../../data/twitter_dev%d" % i, "../../data/twitter_te%d" % i)
#     tr.filter_short_seq(min_len=5)
#     tr.trim_long_seq(10)
#     print("%s : (dev event)/(test event)/(train event) = %3d/%3d/%3d" % (
#     te.data[te.data_ID[0]]['event'], len(dev), len(te), len(tr)))
#     print("\n\n===========%s Train===========\n\n"%te.data[te.data_ID[0]]['event'])
#     model = obtain_LSTMRD(tv)
#     model.train_iters(tr, dev, te, max_epochs=100,
#                       log_dir="../../logs/", log_suffix="TFIDF_GRURD_%s" % (te.data[te.data_ID[0]]['event']),
#                       model_file="../../saved/TFIDF_GRURD_%s.pkl" % (te.data[te.data_ID[0]]['event']))

# model.train_iters(tr, dev, te, max_epochs=100,
#                 log_dir="../logs/", log_suffix="_BertRD",
#                 model_file="LSTMRD_%s.pkl"% (te.data[te.data_ID[0]]['event']))
